{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-近邻 (kNN) 练习题\n",
    " \n",
    "*完成并提交这份工作表（包括输出和外部支持代码）。更多信息，详见[课程网站](http://vision.stanford.edu/teaching/cs231n/assignments.html)。*\n",
    "\n",
    "kNN 分类器包含两个阶段：\n",
    "\n",
    "- 训练阶段，分类器取出训练数据，并简单的记住他们；\n",
    "- 测试阶段，kNN 会将测试图像与所有训练图像进行比较，取出 k 个最相似的训练图，并找到出现频率最高的标签；\n",
    "- k 的取值是通过交叉验证来确定的。\n",
    "\n",
    "在本练习中，你会实现上述步骤，从而理解图像分类的基本流程和交叉验证的方法，并熟练掌握编写高效的向量化代码的方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notebook 需要运行一些启动代码\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 这是 notebook 的 magic 命令，可以让 matplotlib 的图表\n",
    "# 显示在 notebook 中，而不是在一个新窗口里边\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# 还是一些 magic 命令，让 notebook 重载外部的 python 模块；\n",
    "# 见：http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# 加载原始 CIFAR-10 数据\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# 清理变量，以防多次加载数据（这可能会导致内存问题）\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# 完整性测试，打印训练集和测试集的大小\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据集中的一些用例\n",
    "# 每种类型显示几张训练集中的图片\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    \n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 作为练习，为了避免运行时间太长，这里再对数据进行采样，减小规模\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# 把图片数据变成一行 (32, 32, 3) -> 3072\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# 创建 KNN 分类器实例\n",
    "# 注意训练 KNN 分类器其实什么也没干，分类器只是简单地记录了一下数据\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们要用 KNN 分类器对测试用例进行分类。回想一下，我们可以将该过程分为两个步骤：\n",
    "\n",
    "1. 首选，我们计算测试用例与所有训练示例的距离；\n",
    "2. 得到这些距离之后，对于每个测试用例，找到 K 个最邻近的示例（每个示例都有一个对应的标签），并从中选出票数最高的标签。\n",
    "\n",
    "让我们从计算所有训练示例和测试用例之间的距离开始。举个🌰，如果有 **Ntr** 个训练示例和 **Nte** 个测试用例，这个阶段应该返回一个 **Nte x Ntr** 的矩阵，矩阵中坐标为 (i, j) 的元素表示第 i 个测试用例和第 j 个训练示例之间的距离。\n",
    "\n",
    "**注意：对于本 notebook 中需要计算的三个距离，均不能直接使用 numpy 中提供的 np.linalg.norm() 方法。**\n",
    "\n",
    "第一步，打开 `cs231n/classifiers/k_nearest_neighbor.py` 文件，然后实现里边的 `compute_distances_two_loops` 方法，这个方法使用一个（非常低效的）双层循环遍历所有 (测试, 训练) 用例对，并计算每个用例对的距离。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# 打开 cs231n/classifiers/k_nearest_neighbor.py 并实现\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# 测试\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们可以将距离矩阵可视化：每行都表示一个测试用例和所有训练用例的距离\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**相关问题 1** \n",
    "\n",
    "观察距离矩阵的图形，可以发现某些行或者列的亮度更高。（注意：请使用默认的配色方案，颜色越黑表示距离越近，越白表示距离越远。）\n",
    "\n",
    "- 数据中，导致出现明显亮行的原因是什么？\n",
    "- 导致出现明显亮列的原因是什么？\n",
    "\n",
    "$\\color{blue}{\\textit 答:}$ \n",
    "\n",
    "- 明显亮行：说明这个测试用例与所有训练用例的差别都很大\n",
    "- 明显亮列：说明存在训练用例与所有测试用例的差别都很大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# 现在，实现方法 predict_labels，并运行下列代码：\n",
    "# 令 k = 1（最近邻）\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# 计算并打印正确率\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你应该能看到正确率大概为 `27%`。接下来尝试一下更大的 `k` 值，令 `k = 5`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，结果会比 `k = 1` 的时候要好一点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**相关问题 2**\n",
    "\n",
    "我们也可以使用其它距离矩阵，如 L1 范式。\n",
    "设在图片 $I_k$ 上坐标为 $(i,j)$ 的像素值为 $p_{ij}^{(k)}$，\n",
    "\n",
    "所有图片的所有像素值的平均值（像素均值）为 $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "所有图片中相同坐标的像素的平均值（图像均值）为 $$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "\n",
    "公共标准差 $\\sigma$ 和像素标准差 $\\sigma_{ij}$ 的定义类似。\n",
    "\n",
    "下面哪些预处理不会改变使用 L1 距离的最近邻分类器的运行结果：\n",
    "1. 减去像素均值 $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. 减去图像均值 $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. 减去像素均值 $\\mu$ 然后除以公共标准差 $\\sigma$.\n",
    "4. 减去图像均值 $\\mu_{ij}$ 然后除以像素标准差 $\\sigma_{ij}$.\n",
    "5. 旋转数据的坐标轴。\n",
    "\n",
    "$\\color{blue}{\\textit 答：}$\n",
    "\n",
    "上述处理都不会改变运行结果。\n",
    "\n",
    "$\\color{blue}{\\textit 解释：}$\n",
    "\n",
    "$L1(I_1, I_2) = \\sum_p |I_1^p - I_2^p|$\n",
    "\n",
    "对于训练图像 X 和测试图像 y，相同坐标上的像素点 p 的像素值分比为 $I_x^p$ 和 $I_y^p$\n",
    "\n",
    "- 变化 1，处理后的像素值为 $\\tilde{I_x^p} = I_x^p -\\mu$ 和 $\\tilde{I_y^p} = I_y^p -\\mu$，$\\tilde{I_x^p} - \\tilde{I_y^p} == I_x^p - I_y^p $，所以得到的距离矩阵都不会变。同理，变化 2 中相同坐标的像素点减去的值是相同的，距离矩阵也不会变，分类结果更不会变。\n",
    "\n",
    "- 变化 3 ，处理后的像素值为 $\\tilde{I_x^p} = \\frac{I_x^p - \\mu}{\\sigma}$ 和 $\\tilde{I_y^p} = \\frac{I_y^p - \\mu}{\\sigma}$，最后的 L1 距离为 $L1 = \\frac{1}{\\sigma}\\sum |I_x^p - I_y^p|$，由此可见，变化后只是所有图像的距离都变小了相同的倍数，距离矩阵与一个数 $\\sigma$ 进行了除法运算，最后的分类结果不会改变（这是理论上的，但如果用整除的话还是会变的，非整除的话，由于浮点数运算误差的累加，还是可能出现不一样的结果）。\n",
    "\n",
    "- 变化 4，处理后的像素值为 $\\tilde{I_x^p} = \\frac{I_x^p - \\mu}{\\sigma^p}$ 和 $\\tilde{I_y^p} = \\frac{I_y^p - \\mu}{\\sigma^p}$，最后 L1 距离为 $L1 = \\sum \\frac{|I_x^p - I_y^p|}{\\sigma^p}$，这样看来这并不是一个线性变化，所以最后预测的结果可能会发生变化。\n",
    "\n",
    "- 变化 5，处理之后，只是像素交换了坐标，同一位置上的像素集内部元素还是不变的，所以其实距离矩阵也不会变化，预测结果更不会变。\n",
    "\n",
    "> [test_knn.ipynb](./test_knn.ipynb) 中对各种变换进行了实际的测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# 接下来，我们使用部分向l量化，实现用单层循环来计算距离矩阵，\n",
    "# 实现方法 compute_distances_one_loop，并运行如下代码：\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# 为了确认向量化结果的正确性，我们需要确保它的结果与简单方法一致。\n",
    "# 判断两个矩阵相似的方法有很多，其中最简单的一种就是 F 范数。\n",
    "# 两个矩阵的 F 范数是两个矩阵所有元素之差的平方和；\n",
    "# 换句话说，就是把矩阵压成向量，然后计算他们的欧式距离。\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000,)\n",
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# 现在，在 compute_distance_no_loops 方法中实现全向量化版本的代码\n",
    "# 并运行下列代码\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# 同样，通过计算两个矩阵的距离来验证结果\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 22.724715 seconds\n",
      "One loop version took 39.343030 seconds\n",
      "(5000,)\n",
      "No loop version took 0.122352 seconds\n"
     ]
    }
   ],
   "source": [
    "# 比较一下不同实现的效率怎么样\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# 你应该能看到使用全向量化方法之后性能明显提升了\n",
    "\n",
    "# 注意：取决于你使用的机器\n",
    "# 你可能会发现使用单层循序并没有性能的提升\n",
    "# 相反还可能会更慢"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  交叉验证\n",
    "\n",
    "我们已经实现了 kNN 分类器，但之前我们都是直接令 `k = 5`。现在我们来通过交叉验证来确定一下 k 的最佳取值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 8, accuracy = 0.262000\n",
      "k = 8, accuracy = 0.282000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "num_folds_count = np.ceil(X_train.shape[0]/num_folds)\n",
    "for i in k_choices:\n",
    "    list_acc = np.zeros(num_folds)\n",
    "    for j in range(num_folds):\n",
    "        start = int(j * num_folds_count)\n",
    "        end = int((j+1) * num_folds_count)\n",
    "        X_train_folds_train = np.delete(X_train,np.s_[start:end],0)\n",
    "        y_train_folds_train = np.delete(y_train,np.s_[start:end],0)\n",
    "        classifier.train(X_train_folds_train, y_train_folds_train)\n",
    "        dists = classifier.compute_distances_no_loops(X_train_folds[j])\n",
    "        y_test_pred = classifier.predict_labels(dists, k=i)\n",
    "        num_correct = np.sum(y_test_pred == y_train_folds[j])\n",
    "        accuracy = float(num_correct) / num_folds_count\n",
    "        list_acc[j] = accuracy\n",
    "    k_to_accuracies[i] = list_acc\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**相关问题 3**\n",
    "\n",
    "相同的分类器配置下，对于所有的 $k$，下面关于 $k$-近邻（$k$-NN）的说法正确的有哪些？\n",
    "\n",
    "1. k-NN 分类器的决策边界是线性的；\n",
    "2. 1-NN 的训练误差总是比 5-NN 的要低；\n",
    "3. 1-NN 的测试误差总是要比 5-NN 要低；\n",
    "4. 用 k-NN 分类器对一个测试用例进行分类，所需要的时间随着训练集大小的增大而变长；\n",
    "5. 以上全都不对。\n",
    "\n",
    "$\\color{blue}{\\textit 答案:}$\n",
    "\n",
    "2 和 4\n",
    "\n",
    "$\\color{blue}{\\textit 理由:}$\n",
    "\n",
    "- 1：k-NN 的决策边界一般不会是线性的，它是一种非线性的分类器\n",
    "- 2：1-NN 总是找与目标图像差距最小的图片，在训练集上进行分类的话，肯定会找到原图，所以训练误差是 0%；而 5-NN 的话，显然不会是 0；\n",
    "- 3：这个不一定，可能出现意外；\n",
    "- 4：k-NN 要计算测试用例与训练集所有图片的距离，所以训练集越大就越慢；"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
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